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| """ | |
| Copyright (C) 2019 NVIDIA Corporation. All rights reserved. | |
| Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). | |
| """ | |
| import torch.nn as nn | |
| from torch.nn import init | |
| class BaseNetwork(nn.Module): | |
| def __init__(self): | |
| super(BaseNetwork, self).__init__() | |
| def modify_commandline_options(parser, is_train): | |
| return parser | |
| def print_network(self): | |
| if isinstance(self, list): | |
| self = self[0] | |
| num_params = 0 | |
| for param in self.parameters(): | |
| num_params += param.numel() | |
| print('Network [%s] was created. Total number of parameters: %.1f million. ' | |
| 'To see the architecture, do print(network).' | |
| % (type(self).__name__, num_params / 1000000)) | |
| def init_weights(self, init_type='normal', gain=0.02): | |
| def init_func(m): | |
| classname = m.__class__.__name__ | |
| if classname.find('BatchNorm2d') != -1: | |
| if hasattr(m, 'weight') and m.weight is not None: | |
| init.normal_(m.weight.data, 1.0, gain) | |
| if hasattr(m, 'bias') and m.bias is not None: | |
| init.constant_(m.bias.data, 0.0) | |
| elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): | |
| if init_type == 'normal': | |
| init.normal_(m.weight.data, 0.0, gain) | |
| elif init_type == 'xavier': | |
| init.xavier_normal_(m.weight.data, gain=gain) | |
| elif init_type == 'xavier_uniform': | |
| init.xavier_uniform_(m.weight.data, gain=1.0) | |
| elif init_type == 'kaiming': | |
| init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') | |
| elif init_type == 'orthogonal': | |
| init.orthogonal_(m.weight.data, gain=gain) | |
| elif init_type == 'none': # uses pytorch's default init method | |
| m.reset_parameters() | |
| else: | |
| raise NotImplementedError('initialization method [%s] is not implemented' % init_type) | |
| if hasattr(m, 'bias') and m.bias is not None: | |
| init.constant_(m.bias.data, 0.0) | |
| self.apply(init_func) | |
| # propagate to children | |
| for m in self.children(): | |
| if hasattr(m, 'init_weights'): | |
| m.init_weights(init_type, gain) | |